name = EICASLAB
developer = EICAS Automazione S.p.A.
operating_system = Windows/Linux
genre = Technical computing
license = Proprietary
website = [http://www.eicaslab.com/ www.eicaslab.com]
EICASLAB is a laboratory for
automatic controldesign and time-series forecastingdeveloped as final output of the European ACODUASIS Project IPS-2001-42068 [ACODUASIS IPS-2001-42068 : [http://ids.fzi.de/acoduasis/ ACODUASIS Project web-site] ] [ CORDIS Issue n. 44 – September 2003, "Technology opportunities today", page 16: "EICASLAB: A family of CAE tools using automated algorithm generation to design control systems". Published by European Commission – Innovation [ftp://ftp.cordis.europa.eu/pub/focus/docs/res44.pdf on line] ] [EVCA Barometer April 2006, page 5: "An easy to use tool for automated control systems", [http://www.evca.com/images/attachments/tmpl_27_art_41_att_952.pdf on line] ] [CORDIS - ICT results: Results that lead the way: "An easy-to-use tool for automated control systems", Published by European Commission [http://cordis.europa.eu/ictresults/index.cfm/section/news/tpl/article/BrowsingType/Features/ID/81262 on line] ] funded by the European Community, which - during its lifetime - aimed at delivering in the robotic field the scientific breakthrough consisting in a new methodology for the automatic control design [Prof. Francesco Donati (Politecnico of Torino, Italy): "The innovative methodology and the ACODUASIS Project", ACODUASIS Workshop "One Step further in Automatic Control Design", Torino (Italy), 3 October 2005 [http://ids.fzi.de/acoduasis/workshop/aco_ws-prog.htm on line] ] .
To facilitate such a knowledge transfer, EICASLAB was equipped with an “automated algorithm and code generation” software engine [Gabriella Caporaletti (EICAS Automazione, Italy): "The ACODUASIS Project: A professional software tool supporting the control design in robotics", 6th International Conference on Climbing and Walking Robots And the Support Technologies for Mobile Machines. CLAWAR 2003 September 17-19, 2003, Catania, Italy] , that allows to obtain a control algorithm in an easy and friendly way, freeing the designer from the need of a deep knowledge of the theory the methodology and the tool are based upon.
EICASLAB has been adopted in other IST European Research Projects dealing with robotics (ARFLEX IST-NMP2-016880 [ARFLEX Project IST-NMP2-016880 : [http://www.arflexproject.eu ARFLEX Project web-site] /] and PISA Project NMP2-CT-2006-026697 [PISA Project NMP2-CT-2006-026697 [http://www.pisa-ip.org/ PISA Project web-site] : ] ) and it has been used in European industries, research institutes and academia to design innovative control systems described in the scientific and technical literature [ Gabriella Caporaletti (EICAS Automazione, Italy), Rui Neves da Silva and Maria Marques (UNINOVA, Portugal): "Advanced Automated Algorithm Generation Software in the Control of Solar Plant" - MIC 2004 Twenty-Third IASTED International Conference onModelling, Identification and Control [http://www.actapress.com/PaperInfo.aspx?PaperID=16008&reason=500 abstract on line] ] [ Kerscher, Zoellner and Dillman (University of Karlshruhe, Germany), Stella and Caporaletti (EICAS Automazione, Italy):"Model and Control of joints driven by fluidic muscles with the help of advanced automatic algorithm generation software"- CLAWAR 2005 8th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines ] [Kay Ch. Fuerstenberg (IBEO Automobile Sensor GmbH, Germany), Pierre Baraud (Peugeot Citroën Automobile, France), Gabriella Caporaletti (EICAS Automazione,Italy), Silvia Citelli (Fiat Research Center,Italy), Zafrir Eitan (TAMAM/IAI, Israel), Ulrich Lages (IBEO Automobile Sensor GmbH, Germany), Christophe Lavergne (Renault SA, France)"Development of a Pre-crash sensorial system: The CHAMELEON Project", [http://ibeo-as.de/english/downloads/publications/VDI_2001_Fuerstenberg_Wolfsburg.pdf on line] ] [A. Bottero and D. Martinello (COMAU Robotics, Italy):"Industrial robot simulation models for control design and analysis purposes", ACODUASIS Workshop "One Step further in Automatic Control Design", Torino (Italy), 3 October 2005 [http://ids.fzi.de/acoduasis/workshop/aco_ws-prog.htm on line] ] [F. Motto and A. Ramoino (EICAS Automazione, Italy), A. Bottero and D. Martinello (COMAU Robotics,Italy: "Industrial robots control with EICASLAB approach: industrial prototyping and experimentation results", ACODUASIS Workshop "One Step further in Automatic Control Design", Torino (Italy), 3 October 2005 [http://ids.fzi.de/acoduasis/workshop/aco_ws-prog.htm on line] ] [J. Fottner (TELELIFT, Germany), T. Kerscher (University of Karlsruhe, Germany), G. di Gropello and A. Stella (EICAS Automazione, Italy): "Modelling and Control of Automated Guided Vehicles (AGVs) for the transport of meals, laundry and waste in the healthcare domain", ACODUASIS Workshop "One Step further in Automatic Control Design", Torino (Italy), 3 October 2005 [http://ids.fzi.de/acoduasis/workshop/aco_ws-prog.htm on line] ] [ G. Caporaletti and A. Stella (EICAS Automazione, Italy), P. Pina (UNINOVA,Portugal), V. Abadie (CYBERNETIX, France):"Control of a hydraulic servoactuator using an automated algorithm generator", ACODUASIS Workshop "One Step further in Automatic Control Design", Torino (Italy), 3 October 2005 [http://ids.fzi.de/acoduasis/workshop/aco_ws-prog.htm on line] ] [Y. Dodeman and N. Moisan (IPSIS, France), G. di Gropello (EICAS Automazione, Italy):"Synthesis of multivariable control of a thermic power plant",ACODUASIS Workshop "One Step further in Automatic Control Design", Torino (Italy), 3 October 2005 [http://ids.fzi.de/acoduasis/workshop/aco_ws-prog.htm on line] ] [ Prof. R. Bucher and K.Kaufmann – (SUPSI, Switzerland):"Rapid Control Prototyping with EICASLAB and Linux RTAI", ACODUASIS Workshop "One Step further in Automatic Control Design", Torino (Italy), 3 October 2005 [http://ids.fzi.de/acoduasis/workshop/aco_ws-prog.htm on line] ] [Prof. Silvano Balemi (University of Applied Sciences of Southern Switzerland, Lugano-Manno, Switzerland): "Rapid Controller Prototyping Platform for Precision Applications", Proceedings of the 6th euspen International Conference – Baden bei Wien - May 2006, [http://web.dti.supsi.ch/~balemi/ps/EUSPEN06_Balemi.pdf on line] ] .
EICASLAB includes tools for modelling plants, designing and testing
embedded control systems, assisting the phases of the design process of the control strategy, from system concept to generation of the control software code for the final target.
EICASLAB is composed by a main program, called MASTER, able to assist and manage all the design steps by means of 3 tools, respectively:
- the SIMBUILDER tool, devoted to program the simulation models of the plant and of the control algorithms;
- the SIM tool, devoted to the simulation and the evaluation of performances of the control algorithms;
- the POST tool, devoted to the analysis of results through post-processing of recorded simulation data.
upport to system concept
EICASLAB includes the following tools to support the system concept:
* Design of multiprocessor control architectures
* Design of multilevel hierarchical control algorithmsHardware architectures including multi-processors and software architectures including multi-level hierarchical control are considered. The control software is subdivided into functions allocated by the designer to the different processors. Each control function has its own sampling frequency and a time window for its execution, which are scheduled by the designer by means of the EICASLAB
scheduler. Data can be exchanged among the control functions allocated to the same processor and among the different processors belonging to the plant control system. The delay time in the data transmission is considered.The final “application software” generated in C languageis subdivided into files each one related to a specific processor.
upport to system simulation
EICASLAB includes specific working areas for developing, optimizing and testing algorithms and software related to the “plant controller”, including both the “
automatic control” and the “trajectory generation” and the " disturbances" acting on the plant. To perform such a task three different working areas are available as follows.
The PLANT area to be used to simulate the plant dynamic behaviour by means of the “plant fine model”.
The CONTROL area to be used to design the functions related to the automatic control and the trajectory generation.
The MISSION area to be used in order to plan the simulated trials. It is split in two sections, respectively, the Plant Mission and the Control Mission. The first one has to be used to generate the disturbance acting on the plant during the simulated trials and to schedule any other event concerning the plant performance, such as plant parameters variations. The second one is devoted to generate the host command to be sent to the plant control during the simulated trials.
upport to control algorithm design
EICASLAB includes the following tools to support the control algorithm design:
* AAG: Automated Algorithm Generation
* MPI: Model Parameter Identification
* CPO: Control Parameter Optimisation The Automated Algorithm Generation is an option, which - starting from the “plant simplified model” and from the "control required performance" - generates the control algorithm. On the basis only of the plant design data, the applied control design methodology allows to design controllers with guaranteed performance without requiring any tuning in field) in spite of the unavoidable uncertainty which always exists between any mathematical model built on the basis of plant design data and the plant actual performance (for fundamentals on control in presence of uncertainty see [Prof. F. Donati , Prof. D. Carlucci: "Control of norm of uncertain systems", IEEE Transactions on Automatic Control, vol.20-AC, 1975, pp.792- 795 ] [Prof. F. Donati , Prof. M. Vallauri: "Guaranteed control of almost-linear plants", IEEE Transactions on Automatic Control, vol. 29- AC, 1984, pp. 34-41 ] ).The designer can choose among three control basic schemes and for each one he has the option of selecting control algorithms at different level of complexity.
In synthesis, the automatically generated control is performed by the resultant of three actions:- the
open loopaction, which is given by the commands necessary to track the reference signals computed on the basis of the plant simplified model;- the plant disturbance compensation, which is computed on the basis of the disturbance predicted by the plant state observer;- the closed loopaction, which is computed as the action necessary to correct the plant state error with respect to the reference one.
A particular relevance is given to the plant
state observer, the task of which may be extended to estimate and to predict the disturbance acting on the plant. The plant disturbance prediction and compensation is an original control feature, which allows to reduce significantly the control error.
The Model Parameter Identification is a tool which allows the identification of the most appropriate values of the simplified model parameters from recorded experimental data or simulated trials performed by using the “plant fine model”. Let us point out that the above parameter “true” value does not exists. Indeed, the model is an approximated description of the plant and then, the parameter “best” value is depending on the cost functional adopted to evaluate the difference between model and plant. The identification method estimates the best values of the simplified model parameters from the point of view of the closed loop control design.
The Control Parameter Optimization is a tool which allows to perform the control parameter tuning in simulated environment. The optimization is performed numerically over a predefined simulated trial, that is for a given mission (host command sequence and disturbance acting on the plant and any other potential event related to the plant performance) and for a given functional cost associated to the plant control performance.
upport to code generation for the final target
The EICASLAB Automated Code Generation tool provides the C source code related to the control algorithm developed.The final result of the designer work is the “application software” in C language, debugged and tested, ready to be compiled and linked in the plant control processors. The “application software” includes the software related to the “automatic control” and the “trajectory generation” functions. The simulated control functions are strictly the same one that the designer can transfer in field in the actual plant controller.
upport to control tuning
EICASLAB includes the following tools to support the control tuning:
* Slow-Motion View
The Slow Motion View is a tool to be used in the phase of setting up of the plant control, with the aim to allow, step by step and variable by variable, the analysis of the control software performance during experimental trials performed by means of the actual plant.The plant input and output and the host commands sent to the controller are recorded during experimental trials and then they can be processed by EICASLAB as follows. The recorded plant input and output variables are used in the Plant Area inside of the input and output variables obtained by the plant simulation. The recorded host commands are used in the Control Mission area inside of the host command generated by the Control Mission function.Then, when a simulated trial is performed, the control function receives the recorded outputs of the actual plant and the related recorded host commands inside of the simulated ones. Because the control function running in the EICASLAB is strictly the same one, which is running in the actual plant controller, then, the commands resulting from the simulated control function and sent from the simulated control to the simulated plant should be strictly the same of the recorded plant inputs (unless there are numerical errors depending on the differences between the processor where the EICASLAB is running and the one used in the actual plant controller, but the experience has shown that the effects of such differences are negligible).Then, the recorded experimental trial performed by the actual plant controller is completely repeated in the EICASLAB, with the difference that now the process can be performed in slow-motion and, if useful, step by step by using a debugger program.
Automated Code Generation tool can be used to insert the controller code in
Real-time operating system(RTOS), in order to test the control algorithm in the PC environment instead of the final target hardware, performing a Rapid Prototyping test.
It is possible to simulate the plant in EICASLAB and connect the PC to the final controller board: this is the Hardware-in-the-loop test.
Wikimedia Foundation. 2010.
Look at other dictionaries:
Control engineering — Control systems play a critical role in space flight Control engineering or Control systems engineering is the engineering discipline that applies control theory to design systems with predictable behaviors. The practice uses sensors to measure… … Wikipedia
Integrated development environment — Anjuta, a C and C++ IDE for the GNOME environment An integrated development environment (IDE) (also known as integrated design environment, integrated debugging environment or interactive development environment) is a software application that… … Wikipedia
Embedded system — Picture of the internals of an ADSL modem/router. A modern example of an embedded system. Labelled parts include a microprocessor (4), RAM (6), and flash memory (7). An embedded system is a computer system designed to do one or a few dedicated… … Wikipedia
List of programming languages by category — Programming language lists Alphabetical Categorical Chronological Generational This is a list of programming languages grouped by category. Some languages are listed in multiple categories. Contents … Wikipedia
Controller (control theory) — In control theory, a controller is a device which monitors and affects the operational conditions of a given dynamical system. The operational conditions are typically referred to as output variables of the system which can be affected by… … Wikipedia
Dynamic simulation — is the use of a computer program to model the time varying behavior of a system. The systems are typically described by ordinary differential equations or partial differential equations. As mathematical models incorporate real world constraints,… … Wikipedia
Model-based design — (MBD) is a mathematical and visual method of addressing problems associated with designing complex control, signal processing and communication systems. It is used in many motion control, industrial equipment, aerospace, and automotive… … Wikipedia